Affected statuses: Database vs Pipeline

We recently introduced a new filter called 'Pipeline affected status'. This is not be confused with the other 'Affected status' or 'Disease status' filter which is taken from our Patient Database. The 'Pipeline affected status' differs such that you can reconfigure the pipeline to use a different affected status from what is set in the database to produce different cohort reports. This is useful in cases when the affected status applies to multiple phenotypes or diagnoses and you want to to do repeated cohort analysis under different conditions.

Comments

Post a Comment

Popular posts from this blog

Measuring Variant Conservation with GERP Score and Siphy

The more annotations, the better! We've recently added 2 new annotations to assist with variant prioritisation as a measure of variant conservation and these are GERP scores and Siphy. GERP stands for Genomic Evolutionary Rate Profiling. Conceptually, GERP is a method for the identification of slowly evolving regions in a multiple sequence alignment, defined as ‘constrained elements’. "Constrained elements are identified by comparing the observed to the expected rates of evolution for each window, and defining all those regions whose collective observed rates of evolution are significantly lower than would be expected under a null model." More simply, it is a score used to calculate the conservation of each nucleotide in multi-species alignment with ranges from -12.3 to 6.17, with 6.17 being the most conserved. Positive scores (observed fewer than expected) indicate that a site is under evolutionary constraint. Negative scores may be weak evidence of accelerated rates of
Read more

Supplementary information available for download

As part of our datasets for download, we've added some supplementary information (generated as TXT files by the pipeline) to go along with the VCFs and BAMs files. The files contain information such as the cutoffs used to qualify variants as a PASS, which include things like read depth cutoffs, median quality score cutoffs and so on. Furthermore, the files also contain general statistics about total variants passed, the proportion of variants that are exonic, splice sites, number of distinct genes and averages of read depth and median quality. Another file called the 'readReport.summary' includes information about how many were paired, mispaired, aligned, and unaligned. The supplemantary files can be found under the 'Datasets' section.
Read more

Health reports: GWAS

In addition to Clinvar and Snpedia, we've recently added GWAS Catalog to the health reports based on the the rsNumbers for a patient. GWAS is particularly useful in a research context by comparing variant frequencies in the affected population against a control (healthy) population using statistical analysis to establish a hypothetical link between variants and disease traits. In the health report under GWAS, we've added the following columns: disease traits, studies, risk allele, initial sample size, replication sample size, p-value and risk allele frequency. In GWAS it has been shown that false positives are not uncommon (false association between variant and disease) due to uncontrolled biases and so it's important to take into consideration whether any replicate studies were done to give more confidence to the hypothesized association.
Read more